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Announcing the Winners of ‘The NFL Fantasy Football’ Data Challenge

Ocean Protocol

AI / ML offers tools to give a competitive edge in predictive analytics, business intelligence, and performance metrics. Fantasy Football is a popular pastime for a large amount of the world, we gathered data around the past 6 seasons of player performance data to see what our community of data scientists could create.

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What is Alteryx certification: A comprehensive guide

Pickl AI

Alteryx’s Capabilities Data Blending: Effortlessly combine data from multiple sources. Predictive Analytics: Leverage machine learning algorithms for accurate predictions. This makes Alteryx an indispensable tool for businesses aiming to glean insights and steer their decisions based on robust data.

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Cheat Sheets for Data Scientists – A Comprehensive Guide

Pickl AI

A cheat sheet for Data Scientists is a concise reference guide, summarizing key concepts, formulas, and best practices in Data Analysis, statistics, and Machine Learning. It serves as a handy quick-reference tool to assist data professionals in their work, aiding in data interpretation, modeling , and decision-making processes.

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The Age of Health Informatics: Part 1

Heartbeat

Image from "Big Data Analytics Methods" by Peter Ghavami Here are some critical contributions of data scientists and machine learning engineers in health informatics: Data Analysis and Visualization: Data scientists and machine learning engineers are skilled in analyzing large, complex healthcare datasets.

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Statistical Modeling: Types and Components

Pickl AI

Summary: Statistical Modeling is essential for Data Analysis, helping organisations predict outcomes and understand relationships between variables. Introduction Statistical Modeling is crucial for analysing data, identifying patterns, and making informed decisions.

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Artificial Intelligence Using Python: A Comprehensive Guide

Pickl AI

Scikit-learn: A simple and efficient tool for data mining and data analysis, particularly for building and evaluating machine learning models. Data Normalization and Standardization: Scaling numerical data to a standard range to ensure fairness in model training.